The association between two variables y and x, asshown in the scatterplot above, is non-linear. In an attempt totransform the relationship to linearity, a student wouldbe advised to:

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The scenario describes a non-linear association between y and x as shown in the scatterplot, and the goal is to achieve linearity through a transformation. 
Option a: 'use a y^2 transformation'—This is a possible transformation in some contexts, but it would square the dependent variable and is not the most common first choice to linearize a curved relationship like this; it could distort the scale of y and may not produce a straight line. 
Option b: 'leave out the first four points'—Omitting data points to force linearity is inappropriate and constitutes data dredging or cherry-picking; it would bias the analysis and misrepresent the underlying relationship. 
Option c: 'use a log y transformation'—Taking the logarithm of the dependent variable y is a standard, widely used approach to linearize certain monotonic, curved relationships (particularly when the pattern resembles exponential growth or diminishing returns). This transformation can straighten curvilinear patterns, making linear regression more appropriate. 
Option d: 'use a 1/y transformation'—A reciprocal transformation on y can linearize some hyperbolic patterns, but it is less commonly the first choice for typical exponential-like curvature and may produce a different type of nonlinearity or complicate interpretation. 
In this context, the most straightforward and commonly effective method to linearize a monotonic, curved relationship of this kind is to apply a log transformation to y, which aligns with option c.

The association between two variables y and x, asshown in the scatterplot above, is non-linear. In an attempt totransform the relationship to linearity, a student wouldbe advised to:

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